This page contains the implementation used in the paper „Experimental Design for Efficient Identification of Gene Regulatory Networks using Sparse Bayesian Models“ by Florian Steinke, Matthias Seeger, and Koji Tsuda (BMC Systems Biology, to appear).

Approximate inference and, based on that, experimental design for the sparse linear model. This is used for inferring gene networks from disturbance experiments, where such experiments are suggested by the method itself.

If you use this code in a scientific publication, please cite the paper mentioned above. Citation details are on the web site.